5.3. La nulidad matrimonial
5.3.2. La nulidad matrimonial canónica
Although previous research has examined some aspects of the topics that we have discussed here, methodological weaknesses such as sample selection biases may have limited the acceptance of their findings. Our study provides a methodological advance, since we have surveyed a representative sample of entrepreneurs, including those who requested and received pre-start support and those who did not, and have employed a two-stage treatment effects model. However, our study is not free of limitations. First, we examined firms founded in a specific setting. Therefore, results may not be easily generalised to other geographical areas. For example, in our case nascent entrepreneurs seem to be well aware of the existence of a network of start-up supporting agencies and that may not be the case in other regions or for some individuals (Scott and Irwin, 2009).
Moreover, most pre-start support in Navarra is publicly funded and with a universal orientation. In this regard it may not reflect situations where those who apply for support have
to meet certain requirements in terms of the nature of the business (eg science-based ventures). Future studies should therefore consider the extent to which our explanation is universal or is limited to our research context. Nevertheless, our theoretical model can be useful to explain the demand for publicly funded assistance to new firms in contexts where support has a universal orientation. In this regard it may be worth noting that our results concerning the performance effects of different forms of support are consistent with those obtained by researchers in different settings (Chrisman et al, 2005).
While we have used a representative sample of new firms, another limitation of this study is that it relies upon a relatively reduced sample size. While a larger sample size would be desirable, it should be recalled that the sample employed in the research was representative of the target population.
Additionally, in this study we have focused on firms that had already passed the critical three-year hurdle. This period of time may confer a survival bias to our sample. In this sense it would be desirable for a future study to test our predictions by using information from new firms with, for example, less than one year of trading. A straightforward extension of our analysis, which would also provide additional evidence on the robustness of our predictions, would be to explore the impact of different kinds of support on the survival rates of new firms.
Another interesting route for future research would be to investigate the match between demand for and supply of public support. Since we have found that experienced entrepreneurs are less likely to seek pre-start public assistance, one could speculate that existing public support is not suitable for such individuals. This deserves further research.
In addition, though there is much value in concentrating upon publicly funded support to new firms, there is still a need to consider the utilisation of private sector sources of external
support (suppliers, customers, accountants, consultants, etc.). For instance, it would be interesting to examine whether there is any potential interaction between the use of public and private support. The test of this potential interaction effect would require a sample from a geographical context in which the presence of private sector advisors in the provision of external support is greater than that observed in Navarra.
Our results provide some explanation for previous inconsistent findings about the effect of external public support initiatives, as they highlight that the type of support provided matters. While we have proven the important effect of intangible support to entrepreneurs on subsequent venture growth, future research would benefit from the use of more fine-grained measures of types of support. It would also be interesting to include in the analysis variables measuring whether entrepreneurs have founded more than one business sequentially or have done it concurrently (eg Westhead et al, 2004) and to take into account not only the level but also the type of education they received.
In conclusion, the present paper contributes to a better understanding of the role played by information asymmetry faced by the entrepreneur, with respect to resource providers, in explaining the demand for pre-start public support. It shows that industry and entrepreneurial experience as well as prior family business exposure can reduce such asymmetries, thereby reducing the need to request publicly funded pre-start support, at least when it has a universal orientation. In addition, the paper shows the importance of knowledge support to subsequent venture outcomes. Overall, it strengthens the theoretical basis of work in this area by explaining the demonstrated effects with reference to the concept of information asymmetry, the resource-based theory and important constructs in the resource-oriented literature (human and social capital). These different frameworks have not previously been integrated within this literature.
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Figure 1. The conceptual model for the determinants and growth implications of publicly funded pre-start support Prior industry and
entrepreneurial experience
Prior family business exposure
Subsequent firm growth
H3
Publicly funded pre-start support
H4 & H5
Degree of information
asymmetry
Control variables:
individual and firm characteristics
H1 & H2
Figure 2. Stages of the data collection process Stage I: Receipt of potential
de novo ventures using datasets (CNE, RTEA)
Stage I: Cross-checking of datasets, elimination of construction and
transportation firms, and identification of active businesses
Stage II: Initial list of firms founded in Navarra in 2000 and 2001 that were still active in 2005: 595 ventures
Stage II: Identification of non de novo ventures (100 firms): not new, subsidiaries;
exits; telephone line discontinued; tax purposes; moved outside area.
Stage III: Telephone interviews with 485 eligible ventures
Stage III: Outcomes of further telephone interviews: call backs/telephone engaged;
person unavailable; uninterested in being interviewed (261 firms).
224 de novo ventures (46.2%).
192 (39.6%) firms in multivariate analyses due to missing values
Table 1. Mean, standard deviation and Pearson’s correlation matrix a
Mean Std Dev 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
1- Employment growth .432 .613 1.000
2- Pre-start public support .410 .493 .067 1.000
3- Hard support .132 .339 .079 .467 1.000
4- Knowledge support .222 .416 -.030 .639 .194 1.000
5- Information support .307 .462 .032 .797 .193 .482 1.000
6- Foundation year .651 .478 .045 -.053 -.039 -.042 -.054 1.000
7- Gender .698 .460 .079 -.203 -.111 -.248 -.170 -.029 1.000
8- Age 40.797 8.880 -.083 -.202 -.026 -.183 -.224 .131 .140 1.000
9- Education .338 .474 .171 .135 .104 .122 .064 -.063 .036 -.053 1.000
10- Industrial experience 8.892 9.130 -.007 -.250 -.169 -.222 -.174 .133 .240 .431 -.088 1.000 11- Entrepreneurial experience .585 1.179 -.028 -.162 -.051 -.139 -.147 -.057 .161 .313 -.028 .189 1.000 12- Prior family business exposure .566 .497 -.012 -.160 -.135 -.012 -.138 .038 -.058 -.024 .144 .016 -.034 1.000 13- Manufacturing .203 .403 .216 .056 .184 -.101 -.006 .050 .076 .098 -.005 .033 -.061 -.008 1.000 14- Legal status now .395 .490 -.264 .020 .000 .131 .040 -.044 -.300 -.055 -.228 -.009 -.140 .011 -.217 1.000 15- Legal status at inception .414 .494 -.230 -.012 -.015 .109 .014 -.015 -.272 -.068 -.234 .001 -.134 -.034 -.235 .942 1.000 16- Introduction of new products .604 .490 .207 -.050 -.085 .058 -.051 -.047 .098 -.087 .207 -.029 -.040 .089 -.023 -.192 -.179 1.000 17- Plans at inception .324 .469 .107 .210 .027 .189 .203 .148 .016 .148 .250 -.012 .249 .010 -.092 -.226 -.230 .087 1.000
18- Plans now .307 .462 .265 .007 .132 .062 -.045 .036 .192 .112 .314 .037 .191 .149 .072 -.373 -.354 .204 .373 1.000
19- Financial structure .362 .482 .012 -.002 -.113 .028 .060 -.029 .115 .112 .131 .123 .033 .158 .066 -.096 -.083 .089 .013 .005 1.000 20- Family in the firm .081 .191 -.028 .006 -.017 .073 .065 -.056 -.029 -.080 -.020 .034 -.006 -.048 -.081 -.151 -.096 .029 -.078 -.031 -.169 21- Necessity entrepreneur .076 .265 -.064 .160 .151 .147 .157 .061 -.123 -.006 -.017 -.110 -.052 -.112 .033 -.050 -.061 -.096 .044 -.075 .046 22- Firm size at inception 2.778 1.866 -.054 -.044 .033 -.116 -.026 .140 .113 .075 .008 .109 .161 -.025 .137 -.351 -.329 -.004 .161 .254 -.059
20 21
20- Family in the firm 1.000
21- Necessity entrepreneur .087 1.000 22- Firm size at inception -.015 .060
a Significance levels are based on a two-tailed test. For correlations equal or above .140 in absolute value, p < .05. For correlations equal or above .175 in absolute value, p < .01
Table 2. Two-stage treatment effects model of publicly funded pre-start support on firm Prior family business exposure -.401 (.196) *
Necessity entrepreneur .238 (.244)
a Table reports non-standardised β coefficients. Robust standard errors are in parentheses.
Significance levels are based on a two-tailed test for all tests and coefficients. † p < .10, * p <
.05, *** p < .001
Table 3. Two-stage treatment effects model of type of publicly funded pre-start support on firm growth a Hard support Knowledge support Information support Growth equation
Foundation year .046 (.090) .056 (.101) .014 (.114)
Gender -.020 (.088) .115 (.105) -.134 (.132)
Age -.012 (.005) * -.01º (.006) * -.019 (.007) **
Education .115 (.100) -.003 (.112) .135 (.122)
Industrial experience .002 (.005) .009 (.005) † .001 (.006) Entrepreneurial experience -.010 (.057) .010 (.057) -.048 (.066) Prior family business exposure -.129 (.097) -.063 (.095) -.228 (.122) †
Manufacturing .292 (.121) * .282 (.131) * .266 (.150) †
Legal status now -.173 (.087) * -.211 (.104) * -.216 (.113) † Introduction of new products .148 (.085) † .142 (.079) † .133 (.078) †
Plans at inception .006 (.099) .000 (.112) .064 (.111)
Plans now .211 (.113) † .251 (.120) * .210 (.107) *
Financial structure -.020 (.085) -.022 (.083) -.015 (.087) Family in the firm -.202 (.237) -.194 (.239) -.219 (.214) Pre-start public support -.345 (.384) .680 (.224) ** -.941 (.327) **
a Table reports non-standardised β coefficients. Robust standard errors are in parentheses. Significance levels are based on a two-tailed test for all tests and coefficients. † p < .10, * p < .05, ** p < .01
Table 3. Two-stage treatment effects model of type of publicly funded pre-start support on firm growth a (cont.) Hard support Knowledge support Information support Selection equation
Foundation year -.120 (.257) .050 (.204) -.056 (.201)
Gender -.341 (.267) -.499 (.226) * -.371 (.226)
Age .020 (.015) .002 (.013) -.021 (.012) †
Education .504 (.258) * .373 (.230) .067 (.226)
Industrial experience -.035 (.017) * -.022 (.014) -.025 (.014) † Entrepreneurial experience -.033 (.087) -.303 (.147) * -.182 (.080) * Prior family business exposure -.403 (.237) † -.095 (.219) -.446 (.202) * Necessity entrepreneur .650 (.342) † .205 (.314) .506 (.202) *
Manufacturing .613 (.315) * -.323 (.307) -.289 (.349)
Firm size at inception º(log) .221 (.304) -.323 (.151) * .350 (.166) * Legal status at inception .281 (.274) .154 (.237) .068 (.208)
Log pseudolikelihood -223.656 *** -247.157 *** -263.354 ***
Wald test Chi-square (rho=0) 1.640 13.630 *** 7.470 **
N 192 192 192
a Table reports non-standardised β coefficients. Robust standard errors are in parentheses. Significance levels are based on a two-tailed test for all tests and coefficients. † p < .10, * p < .05, ** p < .01, *** p < .001